Wang Yao, Cao Wenwei, Sun Yawen, Chen Xue, Ding Weina, Xu Qun, Zhou Yan, Xu Jianrong, Suo Shiteng
Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127. China.
Department of Neurology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127. China.
Curr Alzheimer Res. 2017;14(9):991-999. doi: 10.2174/1567205014666170329113707.
To investigate the disruption of white matter (WM) integrity in patients with subcortical vascular cognitive impairment (SVCI) with multimodal structural magnetic resonance imaging (MRI), and to explore the relationships between WM damage, structural network disruptions, and general cognitive function decline.
Twenty-eight patients with SVCI and twenty control subjects underwent structural MRI scans and neuropsychological assessment. WM volume (WMV), WM hyperintensities (WMH), lacunar infarcts (LI), diffusion tensor imaging parameters and structural network characteristics were compared between two groups. Correlations between these parameters and general cognitive function were calculated.
WMV, WMH load, LI number, mean fractional anisotropy (FA), peak height and peak location of mean diffusivity (MD) of normal-appearing WM (NAWM) and structural network characteristics were significantly different between the two groups. Disruption of WM microstructure and network characteristics was widespread. WMV, WMH load, mean FA and peak location of MD of NAWM explained about 70% of the variance in structural network characteristics. Shortest path length with LI number explained 47.5% of the variance in z-scores.
SVCI was associated with widespread disruptions of WM integrity in the brain. Network characteristics may be a comprehensive reflection of WM integrity and a superior predictor of general cognitive function, while LI was an independent predictor of cognitive impairment.
采用多模态结构磁共振成像(MRI)研究皮质下血管性认知障碍(SVCI)患者白质(WM)完整性的破坏情况,并探讨WM损伤、结构网络破坏与一般认知功能下降之间的关系。
对28例SVCI患者和20例对照者进行结构MRI扫描和神经心理学评估。比较两组之间的WM体积(WMV)、WM高信号(WMH)、腔隙性梗死(LI)、扩散张量成像参数和结构网络特征。计算这些参数与一般认知功能之间的相关性。
两组之间的WMV、WMH负荷、LI数量、正常表现WM(NAWM)的平均分数各向异性(FA)、平均扩散率(MD)的峰值高度和峰值位置以及结构网络特征存在显著差异。WM微观结构和网络特征的破坏广泛存在。NAWM的WMV、WMH负荷、平均FA和MD的峰值位置解释了结构网络特征中约70%的方差。带有LI数量的最短路径长度解释了z分数中47.5%的方差。
SVCI与大脑中广泛的WM完整性破坏有关。网络特征可能是WM完整性的综合反映,也是一般认知功能的更好预测指标,而LI是认知障碍的独立预测指标。